System and method for automated table game activity recognition

ABSTRACT

Some embodiments relate to a system for automated gaming recognition, the system comprising: at least one image sensor configured to capture image frames of a field of view including a table game; at least one depth sensor configured to capture depth of field images of the field of view; and a computing device configured to receive the image frames and the depth of field images, and configured to process the received image frames and depth of field images in order to produce an automated recognition of at least one gaming state appearing in the field of view. Embodiments also relate to methods and computer-readable media for automated gaming recognition. Further embodiments relate to methods and systems for monitoring game play and/or gaming events on a gaming table.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Stage Application of InternationalApplication No. PCT/AU2017/050452, filed May 16, 2017, which claimspriority to Australian Patent Application No. 2016901829, filed on May16, 2016, the entire contents of each of which are incorporated hereinby reference.

TECHNICAL FIELD

The described embodiments relate generally to monitoring table games. Inparticular, embodiments relate to systems and methods for monitoringevents in table games at gaming venues.

BACKGROUND

Casinos and other such venues are now using surveillance technology andother management software in an effort to monitor players and plan theirbusiness strategy. They seek to deploy real-time behaviour analytics,algorithms (or processes), and player tracking techniques to maximiseplayer revenue, optimise staffing and optimise the allocation of venuefloor space to the types of games which maximise venue revenue. Mostcasino-goers participate in loyalty programs which require them to useplayer cards instead of coins, paper money, or tickets. This has givencasinos the opportunity to record and analyse individual gamblingbehaviour, create player profiles and record such things as the amounteach gambler bets, their wins and losses, and the rate at which theypush slot machine buttons. However, table games are less easilymonitored than either slot machines or button operated gaming machines.

Systems for monitoring and managing table games have typically proven tobe expensive to install and maintain, and have failed to achieve theaccuracy levels which are needed to be truly useful. Other optionsinclude having sensors in the casino chips and other offline yieldmanagement solutions, however these have proven ineffective. Theoperating environment of gaming venues is fast paced, with high amountsof visual and auditory noise and distractions, cards and betting chipscan be in disordered positions on the table, and illumination can varyconsiderably.

Casinos or other such gaming venues conduct several table based gamessuch as Baccarat, Blackjack, Roulette that involve players betting onoccurrence or non-occurrence of specific events. Individual games havetheir own set of defined events that initiate the game, determine theresult of bets placed during the game or terminate a game. Most gamesare conducted by a designated dealer who undertakes certain actionsspecific to each game that may initiate a game, trigger events thatdetermine the result of bets placed or terminate a game.

Casinos and other such gaming venues have an interest in ascertainingtransactional data associated with events occurring on gaming tables orplaying surfaces. This information may assist in the planning of theCasino's business strategy and monitoring behaviour of players.Information regarding the events and outcomes of games on tables mayform a basis for Casinos to ascertain optimal staffing, floor spaceallocation to specific games and other such revenue enhancing or patronexperience-enhancing decisions. One method of ascertaining transactionaldata associated with events occurring on gaming tables employed byCasinos is random sampling by individuals who visually inspect theevents occurring on a subset of tables and report the observedinformation. The reported information may be extrapolated to estimatethe overall level of activity on tables in the Casino. However, suchvisual inspection occurs at intervals of an hour or more and relies onhuman judgement, so there can be inefficiencies with such methods.

Systems for monitoring and managing table games have typically proven tobe expensive to install and maintain, and have failed to achieve theaccuracy levels which are needed to be truly useful. Other optionsinclude having sensors in the casino chips and other offline yieldmanagement solutions, however these have proven ineffective. Theoperating environment of gaming venues is fast paced, with high amountsof visual and auditory noise and distractions, cards and betting chipscan be in disordered positions on the table, and illumination can varyconsiderably.

It is desired to address or ameliorate one or more shortcomings ordisadvantages associated with prior techniques for monitoring events intable games at gaming venues, or to at least provide a usefulalternative.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

In this specification, a statement that an element may be “at least oneof” a list of options is to be understood that the element may be anyone of the listed options, or may be any combination of two or more ofthe listed options.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is not to betaken as an admission that any or all of these matters form part of theprior art base or were common general knowledge in the field relevant tothe present disclosure as it existed before the priority date of eachclaim of this application.

SUMMARY

According to a first aspect some embodiments provides a system forautomated gaming recognition, the system comprising: at least one imagesensor configured to capture image frames of a field of view including atable game; at least one depth sensor configured to capture depth offield images of the field of view; and a computing device configured toreceive the image frames and the depth of field images, and configuredto process the received image frames and depth of field images in orderto produce an automated recognition of at least one gaming stateappearing in the field of view.

According to a second aspect some embodiments provide a method ofautomated gaming recognition, the method comprising: obtaining imageframes of a field of view including a table game; obtaining depth offield images of the field of view; and processing the received imageframes and depth of field images in order to produce an automatedrecognition of at least one gaming state appearing in the field of view.

According to a further aspect some embodiments provide a non-transitorycomputer readable medium for automated gaming recognition, comprisinginstructions which, when executed by one or more processors, causesperformance of the following: obtaining image frames of a field of viewincluding a table game; obtaining depth of field images of the field ofview; and processing the received image frames and depth of field imagesin order to produce an automated recognition of at least one gamingstate appearing in the field of view.

The image frames may comprise images within or constituting the visiblespectrum, or may comprise infrared or ultraviolet images. The depth offield images may comprise time of flight data points for the field ofview, and/or phase information data points reflecting depth of field.The at least one gaming state appearing in the field of view maycomprise one or more or all of: game start; chip detection; chip valueestimation, chip stack height estimation; and game end. Game startand/or game end may be effected by card detection or dolly detection.The table game may be a card game such as poker, blackjack or baccarat,or a non-card based game such as roulette.

Some embodiments relate to a method of monitoring game play on a tablesurface of a gaming table, the method comprising: analysing in real timecaptured images of the table surface to identify a presence of a gameobject in any one of a plurality of a first pre-defined regions ofinterest on the table surface; in response to a game object beingidentified as present in the any one of a plurality of the firstpre-defined regions of interest, recording a time stamp for a gameevent; and transmitting game event data to a server, the game event datacomprising the time stamp, an indication of the gaming event and anidentifier of the any one of a plurality of the first pre-definedregions of interest.

The game object may be a game card or a position marker. The analysingmay comprise identifying a presence of at least one wager object on thetable surface. The at least one wager object may be different from thegame object. The presence of the at least one wager object may beidentified in one or more of a plurality of second pre-defined regionsof interest. The analysing may comprise identifying one or more groupsof wager objects in the one or more second pre-defined regions ofinterest.

The analysing may further comprise: estimating a height of each of theone or more groups of wager object with respect to the table surface;and estimating a number of wager objects present in each group of wagerobjects. The analysing may further comprise identifying a colour of anupper-most one of each group of wager objects.

The method may further comprise automatically estimating a wager amountassociated with each second pre-defined region of interest in which thepresence of at least one wager object is identified, wherein theestimating is based on the identified colour of the upper-most wagerobject of each group of wager objects and the estimated number of wagerobjects in each group of wager objects in the respective second regionof interest.

The captured images may comprise multi-spectral images and the analysingmay further comprise multi frame processing of the multi-spectral imagesto identify the presence of the game object in any one of the pluralityof the first pre-defined regions of interest or second pre-definedregions of interest on the table surface.

Some embodiments relate to a system of monitoring game play on a tablesurface of a gaming table, the system comprising: at least one cameraconfigured to capture images of a table surface; and a computing devicein communication with the camera, said computing device configured toanalyse in real time captured images of the table surface toautomatically identify a presence of a game object in any one of aplurality of a first pre-defined regions of interest on the tablesurface.

The game object may be a game card or a position marker, for example.The computing device may configured to identify a presence of at leastone of wager object on the table surface. The at least one of wagerobject may be different from the game object. The presence of the atleast one wager object is identified by the computing device in one ormore of a plurality of second pre-defined regions of interest. Thecomputing device may configured to identify one or more groups of wagerobjects in the one or more second pre-defined regions of interest.

The at least one camera may further comprise a depth sensing device tocommunicate to the computing device depth data of the game objects withrespect to the table surface. The computing device may be furtherconfigured to estimate a height of each of the one or more groups ofwager object with respect to the table surface. The computing device maybe further configured to estimate a number of wager objects present ineach group of wager objects. The computing device may be furtherconfigured to identify a colour of an upper-most one of each group ofwager objects.

The computing device may configured to automatically estimate a wageramount associated with each second pre-defined region of interest inwhich the presence of at least one wager object is identified, whereinthe estimating is based on the identified colour of the upper-most wagerobject of each group of wager objects and the estimated number of wagerobjects in each group of wager objects in the respective second regionof interest.

The captured images may comprise multi-spectral images and the computingdevice may configured to perform multi-frame processing of themulti-spectral images to identify the presence of the game object in anyone of the plurality of the first pre-defined regions of interest orsecond pre-defined regions of interest on the table surface.

Some embodiments relate to a system for automated monitoring gamingevents on a gaming table comprising: a depth imaging device configuredto capture depth of a plurality of game objects on a gaming region ofthe gaming table; a plurality of visual imaging cameras configured tocapture visual images of the gaming region; a gaming configurationmodule comprising: configuration data associated with a plurality ofgames, configuration of the gaming table, location of regions ofinterest, patterns to recognise game objects, and definition of gamingevents as a change in state of game objects on the gaming table; and acomputer system that receives data from the depth imaging device and theplurality of visual imaging cameras, and is configured to access theconfiguration data in the gaming configuration module to automaticallyrecognise objects on the gaming region and gaming events occurringduring a game.

The methods described herein may be fully automated, so that gameactivity monitoring can occur without any need for human judgement orintervention. However, some human interaction can occur in systemconfiguration steps, such as establishing regions of interest forbetting and for locating game objects, like cards or dollys.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a Gaming Monitoring System.

FIG. 2 is a schematic diagram of a system for automated table gamingrecognition, forming part of the Gaming Monitoring System of FIG. 1.

FIG. 3 is an image of a surface of a Gaming Table that may form part ofa Gaming Environment of the system of FIG. 1.

FIG. 4 is an image of a surface of another Gaming Table that may formpart of the Gaming Environment of the system of FIG. 1.

FIG. 5 is an image of a surface of another Gaming Table that may formpart of the Gaming Environment of the system of FIG. 1.

FIG. 6 is an image of a surface of another Gaming Table that may formpart of the Gaming Environment of the system of FIG. 1.

FIG. 7 is a block diagram of a Depth Sensing Device and Camera for usein the system of FIG. 1.

FIG. 8 is a front view of the inside of a housing of a Depth SensingDevice and Camera according to some embodiments.

FIG. 9 is a block diagram of a Computing Device of the system of FIG. 1.

FIG. 10 is a block diagram of a Message Broker Server of the system ofFIG. 1.

FIG. 11 is a block diagram of a Database Server of the system of FIG. 1.

FIG. 12 is a block diagram of a Web Application Server of the system ofFIG. 1.

FIG. 13 is a screen shot of a Web Application showing an interface formanaging the configuration of another Gaming Table that may form part ofa Gaming Environment of the system of FIG. 1.

FIG. 14 is another screen shot of the Web Application showing aninterface for managing the configuration of another Gaming Table thatmay form part of a Gaming Environment of the system of FIG. 1.

FIG. 15 is another screen shot of the Web Application showing aninterface for managing the configuration of another Gaming Table thatmay form part of a Gaming Environment of the system of FIG. 1.

FIG. 16 is a set of images to illustrate the results of several types ofthresholding operations on a sample image.

FIG. 17 is a set of images to illustrate the results of further types ofthresholding operations on a sample image.

FIG. 18 is a set of images to illustrate the results of erosion anddilation operations on a sample image.

FIG. 19 is a flowchart of an edge detection process.

FIG. 20 is a diagram to illustrate edge detection in part of the processof FIG. 19.

FIG. 21 is an example graph to illustrate example criteria applied inthe edge detection process of FIG. 19.

FIG. 22 is a set of images to illustrate application of a contourdetection process on a sample image with different parameters.

FIG. 23(a) is a plot of a set of points over which a plane estimationprocess is to be applied.

FIG. 23(b) is a plot of the result of the application of the planeestimation process and the orthogonal distances of the estimated planeto points shown in the plot of FIG. 23(a).

FIG. 24 is a flowchart of a Game Monitoring System according to someembodiments.

FIG. 25 is flowchart of a Game Monitoring System according to furtherembodiments.

FIGS. 26(a) and 26(b) are image frames that illustrate the applicationof some card detection processes on another Gaming Table.

FIG. 27(a) is an image frames of another Gaming Table over which cardand chip detection processes may be applied.

FIG. 27(b) is an image frame of the Gaming Table of FIG. 27(a) obtainedby applying a thresholding technique to the image frame of FIG. 27(a).

FIGS. 28(a) and 28(b) are image frames obtained by an infrared camerathat illustrate the application of some card and chip detectionprocesses on another Gaming Table.

FIG. 29(a) is an image frame that may be an input to a Chip DetectionProcess.

FIG. 29(b) is an image frame obtained by application of a binarythresholding operation on the image frame of FIG. 29(a).

FIG. 29(c) is an image frame obtained by application of an erosionoperation on the image frame of FIG. 29(b).

FIG. 29(d) is an image frame obtained by application of a dilationoperation on the image frame of FIG. 29(c).

FIG. 29(e) is an image frame that illustrates the results of applicationof a Chip Detection Process on the input image frame of FIG. 29(a).

FIG. 30 is an image frame that illustrates the results of application ofa Chip Detection Process on a Gaming Table wherein part of the view of agaming table is obstructed.

FIG. 31 is an image frame that illustrates the results of application ofa Chip Detection Process to the Gaming Table of FIG. 30(a) obtainedafter the obstruction in FIG. 30(a) is not in the image frame.

FIG. 32 in an image frame that illustrates the results of application ofa Chip Detection Process to a Gaming Table, wherein the input imageframe is based on an image captured by a visual image camera.

FIG. 33 an image frame that illustrates the results of application of aChip Detection Process to the Gaming Table of FIG. 32, wherein the inputimage frame is based on an image captured by a infrared camera.

FIG. 34 is a flowchart of a Multi Frame Processing Technique accordingto some embodiments.

DETAILED DESCRIPTION

Described embodiments relate generally to monitoring table games. Inparticular, embodiments relate to systems and methods for monitoringevents in table games at gaming venues.

Gaming Monitoring System: FIG. 1 is a block diagram of a GamingMonitoring System 100 according to some embodiments. The system 100 maycomprise a plurality of Gaming Monitoring Setups 105, a GamingMonitoring Infrastructure 115, an Administrator Client 170 and aDatabase Client 180. The Gaming Monitoring Setup 105 comprises a GamingEnvironment 110, a Depth Sensing Device and Camera 120 and a ComputingDevice 130. The system 100 is suited for installation and operation in aone or more gaming rooms of a gaming venue, such as a casino. The gamingrooms each have one or multiple gaming tables located therein and someor each of those tables may form part of a respective Gaming Monitoringsetup 105. Commercially available devices such as Microsoft™ Kinect orAsus™ Xtion or an Infineon™ 3 D Image Sensor REAL3™ other similar depthsensing devices with camera functions can be employed as a Depth SensingDevice and Camera, for example. The depth sensing device and camera 120is coupled with or connected to a Computing Device 130 to receiveinstructions from the Computing Device 130 and transmit recorded data tothe Computing Device 130 using a link 107. For example, a Microsoft™Kinect device may be connected to a Computing Device using a USB port onthe Computing Device.

A gaming venue may have multiple Gaming Environments, for example anarea or room where table games are played, and to monitor each one ofthose Gaming Environments, there may be multiple ones of GamingMonitoring Setup 105. Multiple Gaming Monitoring Setups 105 may becoupled or linked with a common Gaming Monitoring Infrastructure 115using a network link 187. The network link 187 comprises network link117 between the Computing Device 130 and a Message Broker Server 140;and a network link 167 between a Database Server 150 and the ComputingDevice 130. The Gaming Monitoring Infrastructure 115 may also be coupledwith or linked to Gaming Monitoring Setups 105 in two or more differentgaming venues. In some embodiments where a gaming venue may have a largenumber of Gaming Environments 110, multiple ones of Gaming MonitoringInfrastructure 115 may be coupled with different subsets of GamingMonitoring Setups 105 in the same venue.

The Gaming Monitoring Infrastructure 115 comprises the Message BrokerServer 140, the Database Server 150, and a Web Application Server 160.The Message Broker Server 140 may be connected to a plurality ofComputing Devices 130 through the two way Network Link 117. Network link127 may exist between the Message Broker Server 140 and the DatabaseServer 150 to enable the transfer of data or instructions. Network link137 may exist between the Web Application Server 160 and the DatabaseServer 150 to enable the transfer of data or instructions. Each of theservers 140, 150 and 160 may be implemented as standalone servers or maybe implemented as distinct virtual servers on one or more physicalservers or may be implemented in a cloud computing service. Each of theservers 140, 150 and 160 may also be implemented through a network ofmore than one servers configured to handle increased performance or highavailability requirements.

The Administrator Client 170 may be an end user computing device such asa Computer or a Tablet, for example and may be connected to the WebApplication Server 160 through the Network Link 147. The Database Client180 may be an end user computing device or an interface to relay data toother end user computing devices or other databases and may be connectedto the Database Server 150 through the Network Link 157.

Gaming Environment:

Configuration of a Gaming Environment 110 may vary depending on aspecific game being conducted, but most games monitored by any one ofthe embodiments have common elements. FIG. 2 illustrates a system forautomated table gaming recognition 200 in accordance with someembodiments. The main functions of the system of the presently describedembodiment of the invention is to detect when a game starts andfinishes, to detect a location of placed chips, and to estimate thevalue and the height (how many chips) of chip stack. This system isbased on a combination of image processing techniques and sensing devicefeatures.

The Gaming Environment 110 comprises a playing surface or a gaming table210 over and on which the game is conducted. The playing surface 210 iscommonly a substantially horizontal planar surface and may have placedthereon various game objects, such as cards 211 or chips 213 or otherobjects, that may be detected by the Gaming Monitoring System 100. Thedepth sensing device and camera 120 may be mounted on a pillar or post220 at a height so as to position the depth sensing device and camera120 above any obstructions in the field of view of the depth sensingdevice and angled to direct the field of view of the depth sensingdevice and camera 120 somewhat downwardly towards the gaming table 210.The obstructions may be temporary obstructions, such as a dealerconducting a game at a table or a participant of a game or a passer-by,for example. In some embodiments, the depth sensing device or camera 120may be positioned behind and above the dealers' shoulders to get anunobstructed view of a playing surface of the gaming table 210. Theposition of the depth sensing device or camera 120 and the computingdevice 130 may be above or adjacent to other display screens on a pillaror post that are located at that gaming table 210.

FIG. 3 is an image 300 that illustrates part of the playing surface of agaming table configured for the game of Blackjack. The playing surfaceor gaming table comprises a plurality of pre-defined regions of interestand, depending on the nature of the game, may have a specificorientation and function with respect to the operation of the game. Apre-defined region of interest may be designated for detection ofspecific game objects such as game cards or wager objects. For example,in FIG. 3, first pre-defined regions of interest 305 are designated tolocate cards 211 dealt to a player and second pre-defined regions ofinterest 308 are designated to locate the chips or wager objects 213 aplayer may wager in a game. In some embodiments, one or more of apre-defined region of interest may overlap with one or more of anotherpre-defined region of interest. In some embodiments, one pre-definedregion of interest may form a part of another pre-defined region ofinterest.

Participants of the game include players who may place bets and dealerswho conduct the game. To place bets or conduct the game, objectsdescribed as Game Objects are used by the players or dealers. GameObjects may comprise cards 211 in a specific shape with specificmarkings to identify them, Chips or wager objects 213 or other suchobjects may designate amounts players may wager in a game, or maycomprise other objects with a distinct shape that may designate theoutcome of a game such as a position marker or a dolly used in a game ofroulette. The game is conducted through a series of Gaming Events thatcomprises the start of a game, placing of bets by players during a game,intermediate outcomes during a game and the end of a game determiningthe final outcome of the game. During a game, a player may place bets byplacing his wager objects (i.e. betting tokens or chips) in a region ofinterest designated for placing of bets. For example, in the game ofblackjack as shown in FIG. 3, a player may place a bet during a game, byplacing one or more wager objects, such as chips 213, in theirdesignated region 308 for placing a bet. The chips or wager objects maybe arranged in groups or stacks within a region of interest. Often agroup or stack of wager objects will be comprise a common colour ofwager objects.

In certain games, a player may choose a region of interest that isassociated with the likelihood of success and payoffs associated with abet placed. For example, the playing surface 210 of FIG. 6 is a playingsurface with marking for a betting area for a game of roulette.Different regions of interest 308 on the playing surface 600 may havedifferent prospects of success and payoffs for a player's bet. Playingsurfaces may have several different configurations in terms of locationand structure of various regions of interests, depending on differentrules and/or betting conventions associated with the game. FIG. 4 is animage 400 of a gaming table designed for a game of Baccarat. FIG. 5 isan image 500 of a gaming table designed for another game according tosome embodiments.

Depth Sensing Device and Camera:

The Depth Sensing Device and Camera 120 performs the functions ofcapturing visual images and depth information of the field of viewbefore the device. The device 120 is placed before a playing surface ora gaming table in order to capture all the designated regions ofinterest identified on the gaming table. The device 120 comprises aninfrared projector 710, an infrared sensor 720, a camera 730, aprocessor 740, a communication port 750 and internal data links 705 thatconnect the infrared sensor 720 and camera 730 with the processor 740.Internal data link 706 connects the processor 740 with the communicationport 750. The Depth Sensing Device and Camera 120, may capture imagesfrom multiple spectrums of human-visible and/or human-invisible light.For example, the Depth Sensing Device and Camera 120 may capture animage from the visible light spectrum through camera 730 and from theinfrared spectrum through the infrared sensor 720, and consequently mayoperate as a multi-spectral camera.

The Depth Sensing Device and Camera 120 may rely on the Time of Flighttechnique to sense depth of the field of view or scene before it. Theinfrared projector 710 may project a pulsed or modulated light in acontinuous wave that may be sinusoidal or a square wave. Multiple phasesof projected light may be projected and sensed to improve accuracy ofthe depth information. The measured phrase shift between the light pulseemitted by the infrared projector 710 and the reflected pulse sensed bythe infrared sensor 720 is relied upon by the processor 740 to calculatethe depth of the field before the device. In some embodiments, the DepthSensing Device and Camera 120 may include a structured-light 3D scannerrelying on the principle of using a projected light pattern and a camerafor depth sensing. In some embodiments, the Depth Sensing Device andCamera 120 may include a stereo camera that performs the function ofdepth sensing by using images from two or more lenses and correspondingimage sensors. The Depth Sensing Device and Camera 120 may rely on otheralternative means of depth or range sensing or a combination of two ormore techniques to acquire depth information of the Gaming Environmentand of the table playing surface 210 in particular.

The sensed depth information is combined with pixel grid informationdetermined by the processor and presented as an output to thecommunication port 750. The pixel grid information is also combined withthe visual images captured by the camera 730 and presented as outputthrough the port 750 in combination with the depth information. Apartfrom the depth information, the infrared sensor 720 also senses theintensity of the infrared light reflected by the field of view and thisinformation is combined with the pixel grid information by the processor740 and passed to the port 750. The port 750 may be in the form of aphysical port such as a USB port, for example or a wireless transmittersuch as a wireless network adapter. The Depth Sensing Device and Camera120 returns the sensed depth, colour and infrared imaging data indifferent coordinate spaces that may be mapped with each other to get aunified depth, colour and infrared data associated with a specificregion or point in the field of view of the device. FIG. 8 is a frontview of the inside of a housing of a Depth Sensing Device and Cameraaccording to one embodiment 800 and illustrates some of its componentsincluding the Infrared Projector 710, Infrared Sensor 720, and Camera730.

Computing Device:

The data generated by the Depth Sensing Device and Camera 120 isreceived by the Computing Device 130 through the communication port 990.The port 990 may be in the form of a USB port or a wireless adapter thatcouples with the communication port 750 to receive sensor data ortransmit instructions to the Depth Sensing Device and Camera 120.Hardware Components 910 of the computing device 130 comprise Memory 914,Processor 912 and other components necessary for operation of thecomputing device. Memory 914 stores the necessary Software Modules 920which comprise: an Image Processing Library 922; Depth Sensing Deviceand Camera API 924; Runtime Environment Driver 926; Gaming MonitoringModule 928; Batch Scripts 930; Scheduled Jobs 932; and a MessageProducer Module 934.

The Image Processing Library 922 is a set of programs to perform basicimage processing operations, such as performing thresholding operations,morphological operations on images and other programs necessary for theimage processing steps undertaken by Gaming Monitoring Module 928.OpenCV is an example of an Image Processing Library that may beemployed. The Depth Sensing Device and Camera API 924 is a set ofprograms that enables the Computing Device 930 to establish acommunication channel with one or more Depth Sensing Device and Camera920. For example, if a Microsoft™ Kinect™ device is employed as a DepthSensing Device and Camera, then a Kinect for Windows™ SDK will beemployed as the Depth Sensing Device and Camera API 924. This API 924enables the Computing Device 130 to make queries to the Depth SensingDevice and Camera 120 in an appropriate protocol and to understand theformat of the returned results. This API 924 enables the data generatedby the Depth Sensing Device and Camera 120 to be received and processedby the Gaming Monitoring Module 928. The computing device 130 also hasthe Necessary Runtime Environment Drivers 926 to provide the necessarydependencies for the execution of the Gaming Monitoring Module 928. TheGaming Monitoring Module 928 comprises the programs that monitor gamingevents occurring in the course of a game.

The Software Modules 920, also comprises Batch Scripts that may be inthe form of windows power shell scripts or a script in other scriptinglanguages to perform the necessary housekeeping and maintenanceoperations for the Gaming Monitoring Module 928. The batch scripts 930may be executed on a scheduled basis through the Scheduled Jobs 932 thatmay be in the form of windows scheduler jobs or other similar jobscheduling services. The Message Producer Module 934 based oninstructions from the Gaming Monitoring Module 928 produces messagesthat are passed on to the Message Broker Server 140. The MessageProducer Module may be based on a standard messaging system, such asRabbitMQ or Kafka, for example. Based on stored Message BrokerConfiguration 942 in the Configuration Module 940, the Message ProducerModule 934 may communicate messages to the Message Broker Server 140through the Communication Port 990 and the network link 117. TheConfiguration Module 940 also comprises Table Configuration 942 and GameStart and End Trigger Configuration 944. The components of theConfiguration Module 940 are stored in the form of one or moreconfiguration files in the Memory 914. The configuration files may bestored in an XML format, for example.

Message Broker Server:

The Message Broker Server 140 implements a message brokering service andlistens for messages from a plurality of Computing Devices 130 throughthe network link 117. The Message Broker Server 140 may be located onthe same premises as the Computing Device 130 within a common localnetwork or it may be located off-premises (remotely) but still incommunication via the network link 117 established between the twopremises to enable the transfer of messages and data. The Message BrokerServer 140 may be centralised and connected to Computing Devices 130 ina plurality of gaming venues to provide a centralised message brokeringservice. The Message Broker Server 140 has Hardware Components 1010comprising Memory 1014, Processor 1012 and other necessary hardwarecomponents for the operation of the server. The Message Queue Module1020 implements a queue to receive, interpret and process messages froma plurality of Configuration Devices 130. The messages are receivedthrough the Communication Port 1090 with may be in the form of a NetworkAdapter or other similar ports capable of enabling two way transfer ofdata and instructions to and from the Message Broker Server 140. TheMessage Queue Module 1020 may be implemented through a message brokerpackage such as RabbitMQ or Kafka. The Message Queue Module 1020 onreceiving a message comprising transaction information regarding gamingevents occurring on a gaming table initiates a Database Parsing Module1030. The Database Parsing Module 1030 parses the message received bythe Message Queue Module 1020 into a database query that is subsequentlyexecuted on the Database Server 150 through the Network Link 127.

Database Server:

The Database Server 150 serves the purpose of receiving gaming eventdata from the Message Broker Server 140, storing table configurationdata that is managed through the Web Application Server 160 and servingas a repository for Database Client 180 to provide access to the gamingevent data captured by the Gaming Monitoring System 100. The DatabaseServer 150 has Hardware Components 1110 comprising Memory 1114,Processor 1112 and other necessary hardware components for the operationof the server. A Communication Port 1190 may be in the form of a NetworkAdapter or other similar ports capable of enabling two way transfer ofdata and instructions to and from the Database Server 150 through one ormore network links Database Module 1120 may be implemented through adatabase management system such as MySQL™, Postgres or Microsoft™ SQLServer.

The Database Module 1120 holds data comprising Table Configuration Data1122 and Gaming Event Data 1124. Gaming Event Data 1124 comprisestransaction data representing Gaming Events that occur on a gaming tableor a playing surface. The records forming Gaming Event Data may comprisea timestamp for the time a gaming event was recognised; a uniqueidentifier for the gaming table on which the gamin event occurred; anidentifier for the nature of the gaming events such as placing of a bet,intermediate outcome if a game, final outcome of a game; an identifierof a region of interest associated with the gaming event; an estimate ofa bet value associated with a region of interest; and other relevantattributes representing a gaming event.

The Table Configuration Data 1122 comprises: unique identifiers forgaming tables and associated Computing Device 130; location of regionsof interest 308 on the gaming table in the form of polygons andcoordinates of pixels associated with the Depth Sensing Device andCamera 120 forming the endpoints of the polygons; the nature of theregion of interest 308, whether it is a region for placing cards or forplacing chips or for placing a specific gaming object to be detected;nature of game start and end triggering events, whether the start of agame is detected by placing of cards on the region of interest or theplacing of a specific gaming object on a specific region of interest;model contours for game objects such as cards or chips, for example toenable detection by the Gaming Monitoring Module 928; and other relevantdata necessary to represent the parameters relied on by the GamingMonitoring System 100. In some embodiments, the Table Configuration Data1122 and Gaming Event Data 1124 may be held in separate database serversto enable greater scalability and manageability of the Gaming MonitoringSystem 100.

The Database Server 150 also comprises a Table Configuration PropagatorModule 1140 which performs the function of propagating TableConfiguration Data 1122 to the respective Computing Device 130. TheTable Configuration Propagator Module may be implemented through acombination of database scripts and command line scripts that firstgenerate Table Configuration 942, Game Start and End TriggerConfiguration 944 and Message Broker Configuration 946 in the form of aconfiguration file such as an XML file, for example. The generatedconfiguration files may be transferred to the respective ComputingDevice 130 through the Communication Port 1190 replying on a NetworkLink 167. The Network Link 167 may be a local network link if theDatabase Server 150 and the Computing Device 130 are in the same localnetwork or a network link spanning multiple computer networks if theDatabase Server 150 and the Computing Device 130 are located in separatenetworks. The transfer of the configuration files may be effectedthrough an appropriate network protocol such as File Transfer Protocolor SSH File Transfer Protocol, for example.

Web Application Server:

A Web Application Server 160 hosts a Web Application that facilitatesthe configuration and management of the Table Configuration Data 1122 onthe Database Server 150. The Web Application Server 160 has HardwareComponents 1210 comprising Memory 1214, Processor 1212 and othernecessary hardware components for the operation of the server. ACommunication Port 1290 may be in the form of a Network Adapter or othersimilar ports capable of enabling two way transfer of data andinstructions to and from the Web Application Server 160 through one ormore network links. The Web Application Server comprises a WebApplication Module 1220 which comprises web interfaces that enable auser to create and update Table Configuration Data 1122 on the DatabaseServer 150. The web application may be implemented through a webapplication framework such as Django in python or ASP.NET or othersimilar web frameworks, for example. The Web Application Server 160 alsocomprises a Database Parsing Module 1230 that translates instructionsreceived by the Web Application Module 1220 through the web interfaceinto specific database queries or commands that will create or updateThe Table Configuration Data 1122 to reflect the operations undertakenby an Administrator Client 170. The database queries or commands areexecuted on the Database Server 150 through a Network Link 137. TheNetwork Link 137 may be a local area network link if the Database Server150 and the Web Application Server 150 are in a common network or it mayspan multiple networks if the Database Server 150 and the WebApplication Server 150 are located in separate networks.

Web Interface:

FIG. 13 is a screen shot 1300 of a Web Application showing an interfacefor managing the configuration of an embodiment of the Gaming Table thatmay form part of a Gaming Environment 110. Parameters that may berequired in setting up a gaming table and the parameters may include aunique identifier for a table, an IP address of an associated computingdevice, for example are located in the screen region 1310. Part on anXML configuration file that may be propagated to the Computing Device130 to codify the Table Configuration 942 is shown in the screen area1320. A button 1330 may be used to create records for additional gamingtables and a submit button 1340 enables a user to submit a newconfiguration.

FIG. 14 is another screen shot 1400 of the Web Application showinganother interface for managing the configuration of an embodiment of theGaming Table that may form part of a Gaming Environment 110. A deploybutton 1410 may be clicked to deploy a set of saved configurations tothe Computing Device 130 through the network link 167. The delete button1424 may be clicked to delete any saved configurations. 1420 is a sampleof part of another XML file that may be used to store and propagateconfiguration information to Computing Device 130. Screen regions 1412,1414 and 1416 represent depth, colour and infrared image streams fromthe Depth Sensing Device and Camera 130. Details of configurationsassociated with individual streams may be view by clicking the button1422. The configuration details may be deleted by clicking on the button1418. The screen region 1440 allows a user to set up defaultconfigurations for all tables that may be saved by clicking the savebutton 1430.

The button 1430 may be used to save changes to gaming tableconfigurations before deployment.

FIG. 15 is another screen shot 1500 of the Web Application showinganother interface for managing the configuration of an embodiment of theGaming Table that may form part of a Gaming Environment 110. Theinterface shown in screenshot 1500 allows the types, positions andboundaries of the regions of interest to be defined using user interfacetools. Such defined regions of interest then become “pre-defined regionsof interest” as referred to herein once they are saved into the gameconfiguration data. The button 1510 may be clicked to get a refreshedimage of a gaming table if the position of the gaming table with respectto the Depth Sensing Device and Camera 120 changes. The image frame 1515shown in screenshot 1500 includes one or more bounded regions ofinterest 1520, with each bounded region of interest defined by a polygon1525. Custom polygons may be drawn using selectable handles 1560 andadded to a list of polygons 1530, for example. Save button 1540 enablesa user to save changes made to polygons and a remapping button 1550enables a user to remap existing polygons to different locations.

To develop a system for automated recognition of gaming, it is necessaryto understand the behaviour of gaming We deal with two kind of gamingtables. One is a card-based game or card game which is any game usingplaying cards as the primary device with which the game is played, bethey traditional or game-specific. Examples of this type are blackjack,baccarat, and poker. The other type of table game is not based on cards,for example roulette. In this game, players may choose to place bets oneither a single number or a Depth of numbers, the colours red or black,or whether the number is odd or even. To determine the winning numberand colour, a croupier spins a wheel in one direction, then spins a ballin the opposite direction around a tilted circular track running aroundthe circumference of the wheel.

The behaviour of card-based games is as follows.

-   -   Players are free to place bets for some time.    -   Dealer says ‘no more bets’ and starts dealing cards to players.        This is when the game starts.    -   After the game results have been finalized, the dealer collects        losing players' chips and gives out winning chips.    -   Then dealer clear out all the cards. This is when the game ends.

Behaviour of non-card based games, especially roulette.

-   -   Players are to place bets for some time.    -   Dealer spins the wheel and waits for some time until the ball is        close to stopping, then announces ‘no more bets’. This is when        the game starts.    -   After the ball has stopped, the dealer puts a dolly on the table        on the wining number. Winning/losing chips are        allocated/gathered by the dealer.    -   After that, the game ends.

Overall Monitoring Process:

The Gaming Monitoring System 100 in its operation underpins twofundamental aspects: Contour Detection; and Plane Estimation. ContourDetection comprises a set or processes or techniques that may beperformed in real-time or near real-time, to recognise shapes in imagescaptured by the Depth Sensing Device and Camera 120. Near real-timeprocessing may comprise processing with a latency of a few seconds, forexample 2-3 seconds or less after the occurrence of an event. PlaneEstimation comprises a set of processes or techniques that may beperformed in real-time or near real-time, to estimate the position of aplane representing a gaming table or a playing surface based on thedepth data and images captured by the Depth Sensing Device and Camera120. Once the step of Plane Estimation is performed, the obtained planeposition information may be combined with additional depth data capturedby the Depth Sensing Device and Camera 120 to estimate the height of astack of game objects on a gaming table and make an inference about thevalue associated with a stack of game objects such as a stack of chips,for example.

Image Pre-Processing Steps:

Before the Contour Detection or Plane Estimation techniques may beapplied, a number of Image Pre-processing Steps are applied to theimages captured by the Depth Sensing Device and Camera 120. These ImagePre-processing Steps improve the performance and accuracy of processesimplementing the Contour Detection and Plane Estimation techniques.

Thresholding:

One image pre-processing technique that may be employed is Thresholding.The Depth Sensing Device and Camera 120 returns data in colour andinfrared image frames of the Gaming Environment 110. Thresholdingtechniques may be applied to both streams of colour and infrared data.Each pixel in a particular frame of either colour or infrared stream isrepresented by a numeric value that indicates the colour or intensity ofthe pixel. A colour image frame may be converted into a greyscale imageframe before performing a thresholding operation. Global thresholding isone method of implementing thresholding. In Global thresholding eachpixel value is compared with an arbitrary threshold value; if the pixelvalue is greater than the threshold it is assigned a value correspondingto white, for example, 255 in an 8 bit scale; else it is assigned avalue corresponding to black, for example, 0 in an 8 bit scale. Througha series of images 1600, FIG. 16 illustrates an example of the resultsof thresholding on a sample image 1601. Image 1602 is the result of theapplication of Global Thresholding to the Image 1601 using the value of127, the Image 1601 being represented in an 8 bit format. Globalthresholding may not be sufficient for a variety of real worldapplications. An image may have different lighting conditions in variousparts of the image and application of the Global Thresholding techniquemay diminish the parts of an image with low lighting conditions.

Adaptive Thresholding is an alternative to address the limitations ofGlobal Thresholding. In Adaptive Thresholding, threshold values fordifferent, small regions of an image are found and applied to theregions for the purposes of thresholding. The threshold values foradaptive thresholding may be calculated by taking the mean values of thepixels in a neighbourhood of a pixel, or a weighted sum of neighbourhoodpixels where the weights may be taken from a Gaussian distribution. InFIG. 16, image 1603 is an example of the output of application of theAdaptive Means Thresholding technique and image 1604 is an example ofthe output of the application of Adaptive Gaussian Thresholdingtechnique to the original image 1601. Another alternative method ofthresholding is Otsu's Binarization. In this method the thresholding isperformed based on image histograms. Through a series of images 1700,FIG. 17 illustrates an example of the application of Otsu's Binarizationtechnique on a set of sample images 1701 with representative histograms1702. One or more of the alternative thresholding techniques may beapplied at a pre-processing stage and to the colour or infrared imageframes. The Image Processing Library 922 may provide reusable librariesthat implement the proposed thresholding techniques that can be invokedby the Gaming Monitoring Module 928 in the image pre-processing stage.

Morphological Transformations:

Morphological transformations are operations based on the image shape.It is normally performed on binary images. It needs two inputs, one isour original image, second one is called structuring element or kernelwhich decides the nature of operation. Two basic morphological operatorsare Erosion and Dilation

Morphological Transformations are performed on the images captured bythe Depth Sensing Device and Camera 120 in order to enhance the featuresto be detected in the images and improve the performance and accuracy ofContour detection processes. Erosion and Dilation are examples ofmorphological transformations that may be applied during the imagepre-processing stage. Both the erosion and dilation processes requiretwo inputs, image data in the form of a matrix captured by the DepthSensing Device and Camera 120 and a structuring element, or kernel whichdetermines the nature of the morphological operation performed on theinput image. The Kernel may be in the shape of a square or a circle andhas a defined centre and is applied as an operation by traversingthrough the input image.

Erosion:

A morphological transformation of erosion comprises a sharpening offoreground objects in an image by using a kernel that as it traversesthrough an image, the value of a pixel is left to a value of 1 or avalue corresponding to the white colour only if all the values incorresponding to the kernel are 1 or a value corresponding to the whitecolour. Kernels of size 3×3 or 5×5 or other sizes may be employed forthe operation of erosion. Erosion operation, erodes away the boundary offoreground objects. Through a series of images 1800, FIG. 18 illustratesan example of the application of erosion and dilation operators. Anexample of the effect of the erosion operation on an input image 1801may be seen in erosion output image 1802. The operation of erosion maybe performed by a predefined library in the Image Processing Library922. For example, if the OpenCV library is used, the function “erode”may be invoked by the Gaming Monitoring Module 928 to operate on animage captured by the Depth Sensing Device and Camera 120.

To achieve Erosion the kernel slides through the image (as in 2Dconvolution). A pixel in the original image (either 1 or 0) will beconsidered 1 only if all the pixels under the kernel is 1, otherwise itis eroded (made to zero).

Dilation:

An operation of dilation is the inverse of erosion. For example, in adilation operation using a 3×3 square matrix kernel, the pixel at thecentre of the kernel may be left to a value of 1 or a valuecorresponding to the white colour in any one of the values in thecorresponding kernel is 1 or a value corresponding to the white colour.An example of the effect of this operation on an input image 1802 may beseen in the erosion output image 1803. As a consequence of dilation, thefeatures in an image become more continuous and larger. The operation ofdilation may be performed by a predefined library in the ImageProcessing Library 922. For example, if the OpenCV library is used, thefunction “dilate” may be invoked by the Gaming Monitoring Module 928 tooperate on an image captured by the Depth Sensing Device and Camera 120.

Dilation is just the opposite of erosion. Here, a pixel element is 1 ifat least one pixel under the kernel is 1. So it increases the whiteregion in the image or size of foreground object increases. Normally, incases like noise removal, erosion is followed by dilation. Because,erosion removes white noises, but it also shrinks our object. So wedilate it. Since noise elements are removed by erosion they are notreintroduced by dilation, but the object area increases. It is alsouseful in joining broken parts of an object.

The application of a thresholding technique to an image produces abinary image. To further enhance features present in an image, themorphological transformations of erosion and dilation are applied.Advantageously, the morphological transformations assist in reduction ofnoise from images, isolation of individual elements and joiningdisparate elements in an image.

An image contour comprises a curve joining all continuous points alongthe boundary of an object represented in an image. Contours are a usefultool for shape analysis and object detection and recognition. ContourApproximation is used to approximate the similarity of a certain shapeto that of the desired shape in the application. The desired shape maybe in the form of a polygon or a circle or an ellipse, for example. Forbetter accuracy and performance, contour detection operations may beperformed on binary images after Edge Detection operation has beenperformed.

Edge Detection:

Edge detection is an image processing technique for finding theboundaries of objects within images. It works by detectingdiscontinuities in brightness. Among those, Canny is a popularmulti-stage edge detection algorithm (or process) which can be describedas following steps.

Edge detection may be performed by detecting brightness discontinuitiesbetween neighbouring pixels and pixel clusters. Several image processingtechniques may be employed to perform this operation. Some embodimentsimplement the Canny Edge detection operator or process to detect edgesin images captured by the Depth Sensing Device and Camera 120. FIG. 19is a flowchart 1900, that represents a series of steps that are involvedin the implementation of the Canny Edge detection operator. The step1910 involves the preparation of an image for use as an input to theoperator. This step may comprise application of an appropriatethresholding technique to the image, and application of erosion ordilation to improve the performance of rest of the edge detectionprocess. The step 1920 comprises reduction of unwanted noise from theimage. This may be achieved with the application of a 5×5 Gaussianfiltering kernel, for example. This step smoothens the features in theimage and improves the performance of rest of the process. An example ofa Gaussian filtering kernel that may be employed is as follows:

$\begin{bmatrix}2 & 4 & 5 & 4 & 2 \\4 & 9 & 12 & 9 & 4 \\5 & 12 & 15 & 12 & 5 \\4 & 9 & 12 & 9 & 4 \\2 & 4 & 5 & 4 & 2\end{bmatrix}\quad$

The step 1930 comprises estimation of the intensity gradient of theimage. To perform this operation, the input image is filtered by twoSobel kernels. Operation of the kernel G_(x) returns a first derivativeof the image in the horizontal direction and kernel G_(y) returns afirst derivative of the input image in the vertical direction. Thekernels G_(x) and G_(y) that may be used are:

${Gx} = \begin{bmatrix}{- 1} & 0 & 1 \\{- 2} & 0 & 2 \\{- 1} & 0 & 1\end{bmatrix}$ ${Gy} = \begin{bmatrix}{- 1} & {- 2} & {- 1} \\0 & 0 & 0 \\1 & 2 & 1\end{bmatrix}$

Based on the first horizontal and vertical derivatives of the inputimage, the edge gradient G and the direction of each pixel 0 can becalculated as follows:

$G = \sqrt{G_{x}^{2} + G_{y}^{2}}$$\theta = {\tan^{- 1}\left( \frac{G_{x}}{G_{y}} \right)}$

The gradient direction is generally perpendicular to the edges, and maybe rounded to one of four angles which represent the vertical,horizontal and two diagonal directions.

In step 1940, a complete scan of the edge intensity image may be done toremove any unwanted pixels which may not constitute an edge or a desirededge. This is achieved by checking if each pixel is a local maximum inits neighbourhood in the direction of its gradient. As illustrated ingraph 2000 of FIG. 20, Point A is on an edge in a vertical direction; agradient direction is normal to the edge. Points B and C are locatedwithin the gradient direction, therefore point A may be compared againstpoints B and C to observe if it forms a local maximum. If so, it isconsidered for the next stage 1950 in the process, otherwise it may besuppressed by being assigned to point A, a pixel value of 0. The resultis a binary image with pixels of value 1 corresponding to thin edges and0 to no edge.

In step 1950, it is estimated which of the edges detected in theprevious step are true positives, meaning they are more likely representan edge in the real world represented by the input image, rather than afalse positive. In order to perform this operation, two threshold valuesmay be defined: minVal and maxVal. Edges with an intensity gradientgreater than maxVal are considered to be a sure-edge, and those belowminVal may be considered as non-edges and discarded. The edges which liewithin the two thresholds may be further classified as edges ornon-edges by their connectivity property. If they are connected tosure-edge pixels, they are considered to form part of the edge.Otherwise, they may be discarded as false positives. For example ingraph 2100 of FIG. 21, edge A is above maxVal, therefore it may beconsidered a true positive. Although edge C is below maxVal, it isconnected to edge A, and therefore it may also be treated as a truepositive edge, and the entire curve may be considered valid. Althoughedge B is above minVal and is in the same region as that of edge C, itis not connected to any true positive edges and therefore it may betreated as a false positive. Values for minVal and maxVal are chosen toachieve the optimal result. For example minVal may be set to a value ofbetween 20-60 and maxVal may be set to a value between 60-180. Thisstage may also remove noise in the form of small pixel clusters.

Some or all of the steps identified in FIG. 19 may be performed throughprograms available in the Image Processing Library 922. For example, ifthe OpenCV library is used, the “canny” edge detection function call maybe used. Other alternative methods of edge detection may also beutilized as an alternative to canny edge detection to get the sameresult of identification of edges in an input image.

Contour Detection:

After an edge detection operator has been applied to an input image toidentify edges, contour detection processes may be applied to the resultof the edge detection operation to approximate the similarity of shapesin an image to certain model shapes such as a polygon, or a circle forexample.

Contours may be explained as a curve joining all the continuous points(along the boundary), having same colour or intensity. The contours area useful tool for shape analysis and object detection and recognition.It is suggested that for better accuracy, binary images be used as aninput to contour detection algorithms (or processes). So before findingcontours, it is suggested that one should apply thresholding or cannyedge detection. In our application we use border following algorithms(or processes) for the topological analysis of digitized binary images.These algorithms (or processes) determine the surroundness relationsamong the borders of a binary image. Since outer borders and holeborders have a one-to-one correspondence to the connected components ofa pixels and to the holes, respectively, the algorithm (or process)yields a representation of a binary image, from which one may extractsome features without reconstructing the image. The second borderfollowing algorithm (or process), which is a modified version of thefirst, follows only the outermost borders (i.e., the outer borders whichare not surrounded by holes).

Contour Approximation is also performed, which approximates a contourshape to another shape (polygon) with a lesser number of vertices,depending upon the precision we specify. It may be implemented throughthe Douglas-Peucker algorithm as follows:

function DouglasPeucker ( PointList [ ], epsilon ) // Find the pointwith the maximum distance dmax = 0 index = 0 end = length ( PointList )for i = 2 to ( end − 1) { d = perpendicularDistance ( PointList [i],Line ( PointList [1] , PointList [end ])) if ( d > dmax ) { index = idmax = d } } // If max distance is greater than epsilon, // recursivelysimplify if ( dmax > epsilon ) { // Recursive call recResults1 [ ] =DouglasPeucker ( PointList [1... index ], epsilon ) recResults2 [ ] =DouglasPeucker ( PointList [ index ... end], epsilon ) // Build theresult list ResultList [ ] = { recResults1 [1... length ( recResults1 )−1], recResults2 [1... length ( recResults2 )]} } else { ResultList [ ]= { PointList [1] , PointList [end ]} } // Return the result returnResultList [ ] end

In FIG. 22, a series of images 2200 represent various stages of theapplication of the Douglas-Peucker algorithm. Image 2210 is the originalimage that may be used as an input image. The line 2205 in image 2220,represents an approximated curve for the value of epsilon equal to 10%of arc length. The line 2215 in image 2230, represents an approximatedcurve for the value of epsilon equal to 1% of arc length.

Contour estimation operations may be performed using pre-packagedfunctions in the Image Processing Library 922 by invoking them in theGaming Monitoring Module 928. For example if OpenCV is used forimplementing the contour estimation process, then the functions“findContours” or “drawContours” or “approxPolyDP” may be invoked toimplement the process, for example.

Plane Detection Process:

As a precursor to the estimation of the height of a stack of chips onthe gaming table or the playing surface, the Gaming Monitoring System100 estimates the position of a plane which comprises the gaming tableor the playing surface. One method of estimating the equation of theplane is through Principal Component Analysis (PCA). PCA minimizes theperpendicular distances from a set of data to a fitted model. This isthe linear case of what is known as Orthogonal Regression or Total LeastSquare. For example, given two data vectors, x and y, a line in the formof a linear equation with two variables can be estimated that minimizesthe perpendicular distanced from each of the points (x_(i), y_(i)) tothe line. More generally, an r-dimensional hyperplane can be fit inp-dimensional space, where r is less than p. The choice of r isequivalent to the choice of number of components to retain during PCA.

The basic mathematical model of a plane can be formulated as:ax+by +cz+d=0

The values a, b, c, and d need to be estimated to minimize the distancefrom points selected on the gaming table or the playing surface. Basedon a binary version of an image obtained during the chip detectionstage, 100 or more points that are not detected as chips may be chosenas input points for PCA. Since these chosen points are points in a twodimensional space, the depth information associated with points isutilized to obtain co-ordinates in a two dimensional space. Theprinciple of the Pinhole Camera Model is relied on to convert the pointsin the two dimensional space to points in the three dimensional space.Given a point in the two dimensional space with coordinates (x,y), depthvalue Z and (C_(x), C_(y)) as x, y coordinates of the optical centre ofthe Depth Sensing Device and Camera 120; the coordinates (X, Y, Z) ofthe same point in the three dimensional space can be determined usingthe following equations:

$\frac{Y}{Z} = \frac{x - C_{x}}{f}$ $\frac{Y}{Z} = \frac{y - C_{y}}{f}$

FIGS. 23(a) and 23(b) illustrate the application of PCA to a set ofpoints 2310 in a three dimensional coordinate graph 2300. In coordinategraph 2301 in FIG. 23(b), the application of PCA enables identificationof a place 2350 that minimizes the orthogonal distances 2320 between thepoints 2310 and the plane 2350. The plane estimation operations may beperformed using pre-packaged functions in the Image Processing Library922 by invoking them in the Gaming Monitoring Module 928. For example ifOpenCV is used for implementing the contour estimation process, then thefunctions implemented by the class “cv::PCA” may be invoked to implementthe plane estimation process, for example.

To estimate the value of chips, a traditional Euclidean distance ofimages can be used to classify chips. Chip template images will becollected in advance for comparison purpose. Then a k-nearest neighboursalgorithm is used to assign the value of chips. However, there are somechip types with similar colour. To distinguish these types of chips, wefurther utilize the reflectivity of chip type from infrared image toclassify them with the same k-nearest neighbours algorithm.

After having the table plane, distance from centre of each chip to thatplane may be estimated to get the height of a stack of chips. The numberof chips may also be estimated based on this height by linear ornon-linear mapping. Calculating distance from a point to a plane can bederived as follows. Given a plane in 3-dimensional spaceax+by +cz+d=0

-   -   and a point x₀=(x₀; y₀; z₀), the normal vector to the plane is        given by

$v = \begin{bmatrix}a \\b \\c\end{bmatrix}$

-   -   then the distance from that point to the plane is calculated as

$D = \frac{{{ax}_{0} + {by}_{0\;} + {cz}_{0} + d}}{\sqrt{a^{2} + b^{2} + c^{2}}}$

An algorithm (or process) for card detection may be performed. There maybe two type of games: card-based and non-card based games, for example.For card-based games, the algorithm (or process) in 1 can be used todetect cards and trigger game starting events. For roulette, dollydetection will be used to trigger game starting and ending events. Whenthe Gaming Monitoring System detect a dolly (or position marker), it mayinfer that a game may have been started in a few seconds before, forexample 20 to 30 seconds before the detection of the dolly. The dollymay stay in its position until removed by dealer. The removal of thedolly after initial detection may trigger a game ending event. Due tothe reflective feature of dolly, an infrared image may be used insteadof colour image to detect it.

Algorithm 1 Card detection algorithm (process):

-   -   1: Procedure CardDection(img). Input gray-scale image img    -   2: Apply Canny edge detection on img    -   3: Dilate canny output to remove potential holes between edge        segments    -   4: Apply Canny edge detection on img    -   5: Find and approximate contours    -   6: Accept contours that meets following criteria:        -   Area of contour must within area size of cards, for example            between 40 to 70 cm² or between 50 to 60 cm²        -   Contour should have 4 vertices after approximation        -   The cosine of the angle between joint edges must be small,            for example between −0.2 to 0.2 or between −0.1 to 0.1.

Algorithm 2 Dolly detection algorithm (process):

-   -   1: procedure DollyDection(img). Input infrared image img    -   2: Apply global thresholding on img    -   3: Erode the output to remove small objects.    -   4: If there is any left object that meets size criteria, it will        be a detected dolly.    -   5. Chip Detection To detect chips, we use adaptive thresholding        to segment chips from background. The output binary will be        eroded and dilated to remove small objects. After, all blobs        that meet size-criteria will be detected as chips. This        algorithm (or process) can be applied on both color and infrared        images. Below sub-sections are re-views of techniques used and        layout the detail of the algorithm (or process) is described in        Algorithm 3.

Algorithm 3 Chip detection algorithm (or process) (process):

-   -   1: procedure CardDection(img). Input gray-scale image img    -   2: Apply adaptive thresholding on img    -   3: Erode and dilate the output to remove small objects.    -   4: If there is any left object that meets size criteria, it will        be a detected chip.

Specific criteria such as the size criterion or the cosine of an anglecriterion may be stored in the Configuration Module 940 as part of theTable Configuration 942.

The chip height estimation algorithm may be seen in Algorithm 4:

Algorithm 4 Chip stack height estimation algorithm (process):

-   -   1: procedure ChipHeightEstimate(img). Input depth image img    -   2: Convert selected table points which are not chips to 3D        coordinates    -   3: Fit these point to a plane called table plane    -   4: Find distance from each centre of chip stack to this plane to        get chip stack height    -   5: Divide the height from the height of single chip to estimate        number of chips in stack

FIGS. 26(a) and 26(b) are image frame 2600 and 2650 respectively of anembodiment of a gaming table where card detection and chip detection isbeing performed. In FIG. 26(a) the boundary 2620 represents an area ofthe image frame where a card has been detected. The boundary 2630represents an area of the image frame where a chip has been detected.The card 2610 has not been detected by the card detection algorithm (orprocess) because it has not been fully presented to the Depth SensingDevice and Camera 120 and is in the process of being dealt on the gamingtable 210. In FIG. 26(b) a card 211 different from the card presented inFIG. 26(a) has been detected and is surrounded by the boundary 2620.Chips 213 in FIG. 26(b) are in different positions from the chips inFIG. 26(a) and have been detected with the boundary 2630.

FIGS. 27(a) and 27(b) represent image frames 2700 and 2701 respectivelyof an embodiment of a gaming table where card detection and chipdetection is being performed. FIG. 27(b) is the result of application ofany one of the binary thresholding techniques to FIG. 27(a). Shape 2710in FIG. 27(b) may be potential false positive detections for cards 211.These false positives may be eliminated by the step 6 of Algorithm 1that compares the area of a shape with the expected area of a card.Similarly shapes 2720 may be eliminated as false positive detections forchips 213 by a comparison of the area of the shapes 2720 and theexpected area of chips 213. FIG. 28(a) represents an infrared image ofan embodiment of a gaming table 2800 with gaming objects such as cards211 and chips 213. In FIG. 28(b) the chips 211 have been identified bythe Chip Detection Algorithm 3 and greyed out to indicate the detection.Results of the application of game object detection processes on colourimage frames may be combined with infrared image frames to improve theaccuracy of the detection and estimation of chip stack height.

FIGS. 29(a) to 29(e) illustrate the application of the Chip DetectionProcess to an image frame 2900 according to some embodiments. An imageframe 2900, may be captured by the Depth Sensing Device and Camera 120.FIG. 29(b) is an image frame 2901, obtained by application of a binarythresholding operation to the input image frame 2900 of FIG. 29(a).Image frame 2902 of FIG. 29(c) is obtained by application of an erosionoperation to the image frame 2901 of FIG. 29(b). FIG. 29(d) is an imageframe 2903, obtained by application of a dilation operation to the inputimage frame 2902 of FIG. 29(c). The combination of erosion and dilationoperations helps reduce noise from the input image frame and makes theobservable features such as chips more prominent. FIG. 29(e) is an imageframe 2904, that illustrates results of the chip detection process.Detected chips 213 are identified by the outline 2920. Shapes 2910 ofFIG. 29(d) are not identified as chips because they may not meet acriteria as part of the process of chip detection. The criteria may be asize range for a shape to be detected as a chip, or other similardistinguishing criteria.

Some embodiments follow the steps in the flowchart 2400 in FIG. 24 tomonitor gaming events of gaming tables. The Frame Grabber 2410 collects3 streams of frames, comprising colour video frames, depth frames, andinfrared frames. The raw colour images have 1920×1080 resolution. Thedepth stream provides a depth value of every point in the visible area.The depth value is the distance of the observed point from the sensor.The Infrared stream brings the ability to view more clearly into darkerportion of the field of view.

Game Start Detection occurs at 2420. To detect when the game starts, thesome embodiment use cards as a trigger. For games without cards likeRoulette, a dolly may be detected when the game finishes and a gamestart event is triggered back in n^(th) frame.

Chip Detection and Estimation occurs at 2430. This is the process ofdetecting the location of chips, estimating chip cash value and how manychips in that chip stack. The algorithm (or process) is based onadaptive thresholding and morphology operation. This could be applied onboth colour and infrared images.

Chip value estimate is performed at 2460 by using template matching incolour images and reflective features of each chip types in infraredimage. Chip height estimate is then calculated based on distance betweenthe top surface of chip and the table plane.

Game Ending Detection 2460 is the process opposite to game startingdetection. When the dealer clears out the cards, it triggers the gameending event. For roulette, the detection of removal of the dolly willtrigger this event.

Game Start and End Detection:

FIG. 25 represents a flowchart of a Gaming Monitoring Process 2500 thatsome embodiments may implement to monitor events on gaming tables. Thetwo significant steps in the process are Game Start Detection 2520 andGame End Detection 2560. The specific nature of the events that definegame start and end triggers may be stored in the Game Start and EndTrigger Configuration 944 and referred to by the Gaming MonitoringModule 928 to estimate of a game has started or ended on a table. Forexample, for a table designated for the game of blackjack, the presenceof one or more cards in an image frame extracted in a step 2510 may betreated as the start of a game. Likewise, after the start of a game, theabsence of any cards in an image frame may be treated as the end of agame for the purpose of Game End Detection 2560. For games not based oncards such as roulette, the presence of other game objects such as adolly may be used the start and end triggers for a game. The specificshape and nature of a game start or end trigger initiating game objectmay be saved in the Game Start and End Trigger Configuration 944 of theConfiguration Module 940 of the Computing Device 130. Theseconfigurations may be managed through the Web Application Server 160 onthe Database Server 150 and be subsequently transferred to the ComputingDevices 130 through the Table Configuration Propagator Module 1140.

Overall Monitoring Process:

The Gaming Monitoring Process 2500 starts with a step of Extraction ofan Image Frame 2510. This step comprises capturing an image of a GamingEnvironment 110, by a Depth Sensing Device and Camera 120 andtransmission of the captured image via the link 107 to the ComputingDevice 130 available for analysis by the Gaming Monitoring Module 928.The images may be captured in a colour format or in infrared format orboth or any other format the Depth Sensing Device and Camera is capableof capturing an image in. Next step 2520 comprises the detection of thestart of a game by application of the Algorithm 1: Card DetectionAlgorithm or Algorithm 2: Dolly Detection Algorithm, for example. If thebeginning of a game is detected, them a step of automatically Estimatingthe Plane of the gaming table or playing surface 2530 is performed. Step2530 may be performed using PCA discussed above and the results may bestored in the memory of the Computing Device 130.

A next step 2540 comprises detection of chips. This step may beperformed through Algorithm 3: Chip Detection Algorithm. If a chip isdetected, then a next step 2550 may be to estimate the value of thestack of chips in the image. This step is performed by implementingAlgorithm 4: Chip stack height estimation algorithm or process which maybe implemented in real-time or near real-time. Part of the process mayalso include automatically estimating the colour of a chip or wagerobject on top of a stack and retrieving from the Table Configurationmodule 942 the value associated with the colour. The retrieved value maybe multiplied with the estimated number of wager objects or chips in thestack to automatically estimate the value of the entire stack. Theresults of the application of this algorithm or process may be stored inthe memory of the Computing Device 130 and recorded as an event or atransaction. The detected stack of chips is also associated with aregion of interest on the table to distinguish it from other stacks ofchips on the same gaming table.

Multiple stacks of chips or wager objects on a single region of interestmay be separately detected and the estimated values of each stack may berecorded separately with a unique identifier of the region of interest.Alternatively, the value of multiple stacks of chips on a single regionof interest may be aggregated and recorded with a unique identifier ofthe region of interest. The height of multiple stacks of chips may beestimated in one iteration, or in several iterations of theimplementation of Algorithm 4. After estimating the value of the stackof chips, another image frame is grabbed through step 2514 and the GameEnd Detection step 2560 is performed. Game End Detection may beperformed by looking for an absence of any cards or by the absence of adolly on the gaming table in the image frame grabbed at step 2514, forexample. If the Game End is not detected, then another image frame isgrabbed at step 2512 and this is followed by another iteration ofDetection of Chips at step 2540.

If the end of a game is detected, then in step 2570, the event datacaptured during the monitoring process is sent to the Database Server150 to be stored as Gaming Event Data 1124 by via the Message BrokerServer 140. In other embodiments, the step of reporting transaction tothe Database Server 150 may occur as the events are being detected inreal-time or near real-time manner. This event data may comprisetimestamps the event occurred, the regions of interests over which chipswere detected, the estimated value of stacks of chips, game start andend times and other relevant parameters captured by the GamingMonitoring Module 928. After reporting transactions to the DatabaseServer, the Gaming Monitoring System may continue to monitor the gamingtable and await the beginning of the next game at steps 2510 and 2520.Examples of some records of event data captured during a game are asfollows:

Game Record 1<Table ID: R.2745; Game ID: 17.0.20170421150202; Game StartTime: 2017-04-21 15:02:02; Game End Time: 2017-04-21 15:04:02>

Game Object Record 1<Table ID: R.2745; Game ID: 17.0.20170421150202;Region of Interest ID: Player-1, Wager Object Value: 5; Wager ObjectCount: 5>

Game Object Record 2<Table ID: R.2745; Game ID: 17.0.20170421150202;Region of Interest ID: Player-2, Wager Object Value: 10; Wager ObjectCount: 2>

In the above examples of game event data, the Game Record 1 with aunique identifier “Game ID: 17.0.20170421150202” is associated with aunique gaming table or playing surface identified by the uniqueidentifier “Table ID: R.2745”. The Game Record also comprises game startand end time stamp values. Associated with Game Record 1 are two gameobject records: Game Object Record 1 and Game Object Record 2. GameObject Record 1 represents an estimate of 5 wager objects of value 5detected in a region of interest with a unique identifier “Player-1”.Game Object Record 2 represents an estimate of 2 wager objects of value10 detected in a region of interest with a unique identifier “Player-2”.

Multi Frame Processing:

The approaches to game object detection described above take intoaccount single frames or snapshots to perform any processing. Theresults produced by this approach can be further improved to avoid falsepositives and achieve greater accuracy through Multi Frame Processingtechniques. This technique comprises processing every frame for gameobject detection and chip height estimation and logging of resultsobtained after every processing iteration. Certain processing rules maybe applied to the logged results to improve the game object detectionand chip stack height estimation steps. For example, averaging of chipstack values estimated over several frames may be used to compensate fora few frames with erroneous observations. Also, in games where the firstcard detection may be delayed, the Gaming Monitoring Module 928 maytraverse back in time to a captured frame perform card detection againto identify the beginning of a new game retrospectively. Moving objectstriggering a game may be reduced by detecting cards in a plurality ofregions of interest and only treating a game as initiated if a certainratio of regions of interest are detected with cards; for example 3 outof 8 regions, or 2 out of 12 regions.

Multi Frame Processing techniques may also be employed to detect gameobjects that may be temporarily obstructed from the view of the DepthSensing Device and Camera 120. FIG. 31 is an image frame 3000 thatrepresents a view of a Gaming Table with an obstruction in the form of adealer's head 3010. FIG. 31 is an image frame 3100 that represents aview the Gaming Table of FIG. 30 taken a few seconds before or after theimage frame 3000. Image frame 3100 does not have the obstruction 3010and the application of a chip detection algorithm results inidentification of a chip 3113 that was not previously detected in theimage frame 3000. The combination of the results obtained by theapplication of game object detection processes on image frames that mayhave been obtained over a small period of time, such as over 2-4 secondsor over 1-2 seconds or within a second, may be used to improve theoverall accuracy of a game object detection process.

The Multi Frame Processing Technique described above may also beextended to image frames obtained from different sources, such as imagesobtained from a visual image camera and an infrared camera or imagesobtained from more than one Depth Sensing Devices and Camera 120, forexample. FIG. 32 is an image frame 3200 obtained from a visual imagecamera that may be a part of a Depth Sensing Device and Camera 120. Inthis image 3200, the application of a chip detection process may producea false positive result by identifying a chip 3210. FIG. 33 is an imageframe 3300 obtained from an infrared camera after image pre-processingsteps and edge and contour detection techniques have been applied to araw image frame. The application of chip detection processes in theimage frame 3300, does not produce the false positive result produced inimage frame 3200. Thus, a combination of results obtained by applicationof game object detection processes on multiple different (types of)image frames may be effective in some embodiments to improve theaccuracy of game object detection results.

The flowchart of FIG. 34 is an example of an implementation of a MultiFrame Processing technique 3400 that some embodiments may implement.Step 3410 comprises acquiring two image frames A and B through the DepthSensing Device and Camera 120. The image frames A and B may be from avisual image camera or an infrared camera and may be separated in timeof capture by a few milliseconds, for example. Alternatively, the imageframes A and B may have been obtained from a visual image camera and aninfrared camera respectively at the same point of time, for example.Both image frames A and B are taken from substantially the sameperspective and represent the same gaming table or playing surface.

Step 3420 comprises the application of a game object detection processto each region of interest in image frames A and B. The game objectdetection process may be a card detection process, or a chip or wagerobject detection process, for example Results obtained from a gameobject detection process for a given region of interest may include oneor more of: identification of the presence of a game object in a regionof interest, a number of game objects in a region of interest, a numberof distinct groups of game objects within the region of interest, anumber of game objects in each identified group, and/or colour of a gameobject, for example. If the game object detection process does notidentify a game object in a particular region of interest, then theresult of that process may be a null result (no result reported) or aresult indicating zero game objects detected. Specific pre-definedregions of interested may be designated for identification of thepresence of a specific game object. For example, a pre-defined region ofinterest may be designated for identification of game cards as part ofthe game object detection process. Other pre-defined regions of interestmay be designated for identification of wager objects as part of thegame object detection process.

Step 3430 comprises a comparison of the results obtained by theapplication of the game object detection process on image frames A and Bat step 3420. If the results obtained from image frame A and B match;for example if location, number and colour of game objects detected inimage frame A and B are identical; then in step 3440 the obtainedresults are accepted and reported as a game event by the ComputingDevice 130.

If the results obtained from image frame A and B do not match, either interms of location or number or colour of game objects detected, then instep 3450 an image frame C is acquired from the Depth Sensing Device andCamera 120. Image frame C may be captured from a different source,visual image camera or infrared camera, for example; or may have beentaken at a point of time separated from the time at which image frames Aor B were acquired by a few milliseconds, for example. All images A, Band C are taken from substantially the same perspective and representthe same gaming table or playing surface.

In step 3460, the game object detection process applied at step 3420 isalso applied to image frame C. At step 3470, the results obtained byapplication of the game object detection process on image frames A, B,and C, at steps 3420 and 3460 are compared. If results obtained fromimage frame A match results obtained from image frame C, then theresults obtained from image frame B are discarded as an anomaly andresults obtained from image frame A or C are accepted at step 3440. Ifresults obtained from image frame B match results obtained from imageframe C, then the results obtained from image frame A are discarded asan anomaly and results obtained from image frame B or C are accepted atstep 3440. If, none of the results obtained from image frames A or B orC matchup, then a fresh set of images may be acquired at step 3410 toobtain better game object detection results.

In this description of some embodiments, reference is made to diagramsthat are certain abstractions of the software and hardware system thatcombine to form the embodiments. These abstractions are to be understoodas such, and are presented here to help understand the embodiment andenable their reproduction or implementation. The division of the systeminto blocks is in accordance with functions of the system and ispresented as such with the understanding that in an implementation, oneneed not have software or hardware component that are logically orphysically divided in such a manner. Likewise, the physical equipmentinstallation is but one possibility, since the functions of the systemcan be distributed differently without departing from the disclosure.For convenience, the system and specific examples are explained in thecontext of the games of Blackjack, Baccarat and Roulette. It isunderstood that some or all of the components of the system can beapplied to other table games as well.

Some embodiments may further take an initial background image for thecamera view. This background image could be of an empty table. Thisimage may be warped to a birds-eye view based on key features within theimage (the table layout). This new image may be referred to as thenormalized background image and may thereafter be used for backgroundabstraction.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the above-describedembodiments, without departing from the broad general scope of thepresent disclosure. The present embodiments are, therefore, to beconsidered in all respects as illustrative and not restrictive.

The invention claimed is:
 1. A method of monitoring game play on a tablesurface of a gaming table, the method comprising: analysing in real timeby a computing device in communication with at least one cameraconfigured to capture images of the table surface, captured images ofthe table surface to identify a presence of a game object in any one ofa plurality of first regions of interest on the table surface; inresponse to the game object being identified as present in the any oneof a plurality of first regions of interest, recording a time stamp fora game event; transmitting, by the computing device, game event data toa server, the game event data including the time stamp, an indication ofthe game event and an identifier of the any one of a plurality of firstregions of interest; capturing depth of field images of the tablesurface using at least one depth sensor; and wherein the analysingincludes identifying one or more groups of wager objects in one or moresecond regions of interest, estimating a height of each of the one ormore groups of wager objects with respect to the table surface based onthe captured depth of field images, and estimating a number of wagerobjects present in each group of wager objects based on the estimatedheight of each of the one or more groups of wager objects with respectto the table surface; and wherein the one or more groups of wagerobjects are different from the game object.
 2. The method of claim 1,wherein the game object is a game card or a position marker.
 3. Themethod of claim 1, wherein the analysing further comprises identifying acolour of an upper most wager object among each group of wager objects.4. The method of claim 3, further comprising: automatically estimating awager amount associated with each second region of interest in which atleast one wager object is identified, wherein the automaticallyestimating is based on the identified colour of the upper-most wagerobject among each group of wager objects and the estimated number ofwager objects in each group of wager objects in a respective secondregion of interest.
 5. The method of claim 1, wherein the analysing inreal time by a computing device comprises: detecting one or more edgescorresponding to the game object; and approximating a contour of the oneor more edges to a contour model to identify the game object.
 6. Anon-transitory computer-readable storage medium storing instructionsthat when executed by a computer cause the computer to perform themethod of claim
 1. 7. A method of monitoring game play on a tablesurface of a gaming table, the method comprising: analysing in real timeby a computing device in communication with at least one cameraconfigured to capture images of the table surface, captured images ofthe table surface to identify a presence of a game object in any one ofa plurality of first regions of interest on the table surface; inresponse to the game object being identified as present in the any oneof a plurality of first regions of interest, recording a time stamp fora game event; transmitting, by the computing device, game event data toa server, the game event data including the time stamp, an indication ofthe game event and an identifier of the any one of a plurality of firstregions of interest; wherein the captured images include multi-spectralimages; and wherein the analysing includes multi frame processing of themulti-spectral images to identify the presence of the game object in anyone of the plurality of the first regions of interest or second regionsof interest on the table surface.
 8. A system of monitoring game play ona table surface of a gaming table, the system comprising: at least onecamera configured to capture images of the table surface; and acomputing device in communication with the at least one camera, saidcomputing device configured to analyse, in real time, captured images ofthe table surface to automatically identify a presence of a game objectin any one of a plurality of first regions of interest on the tablesurface; wherein said at least one camera includes a depth sensingdevice to communicate, to the computing device, depth data of wagerobjects with respect to the table surface; wherein said computing deviceis configured to identify one or more groups of wager objects in one ormore second regions of interest, estimate a height of each of the one ormore groups of wager objects with respect to the table surface based onthe depth data of wager objects with respect to the table surface, andestimate a number of wager objects present in each group of wagerobjects based on the estimated height of each of the one or more groupsof wager objects with respect to the table surface; and wherein the oneor more groups of wager objects is different from the game object. 9.The system of claim 8, wherein the game object is a game card or aposition marker.
 10. The system of claim 8, wherein the computing deviceis further configured to identify a colour of an upper-most wager objectamong each group of wager objects.
 11. The system of claim 10, whereinthe computing device is further configured to automatically estimate awager amount associated with each second region of interest in which atleast one wager object is identified, and the automatic estimate isbased on the identified colour of the upper-most wager object among eachgroup of wager objects and the estimated number of wager objects in eachgroup of wager objects in a respective second region of interest. 12.The system of claim 8, wherein the computing device is configured toanalyse, in real time, the captured images of the table surface bydetecting one or more edges corresponding to the game object; andapproximating a contour of the one or more edges to a contour model toidentify the game object.
 13. A system of monitoring game play on atable surface of a gaming table, the system comprising: at least onecamera configured to capture images of the table surface; and acomputing device in communication with the at least one camera, saidcomputing device configured to analyse, in real time, captured images ofthe table surface to automatically identify a presence of a game objectin any one of a plurality of first regions of interest on the tablesurface; wherein the captured images include multi-spectral images; andwherein the computing device is configured to perform multi-frameprocessing of the multi-spectral images to identify the presence of thegame object in any one of the plurality of the first regions of interestor second regions of interest on the table surface.